Tag Archives: view

#435765 The Four Converging Technologies Giving ...

Posted on September 20, 2019 by Android

How each of us sees the world is about to change dramatically.

For all of human history, the experience of looking at the world was roughly the same for everyone. But boundaries between the digital and physical are beginning to fade.

The world around us is gaining layer upon layer of digitized, virtually overlaid information—making it rich, meaningful, and interactive. As a result, our respective experiences of the same environment are becoming vastly different, personalized to our goals, dreams, and desires.

Welcome to Web 3.0, or the Spatial Web. In version 1.0, static documents and read-only interactions limited the internet to one-way exchanges. Web 2.0 provided quite an upgrade, introducing multimedia content, interactive web pages, and participatory social media. Yet, all this was still mediated by two-dimensional screens.

Today, we are witnessing the rise of Web 3.0, riding the convergence of high-bandwidth 5G connectivity, rapidly evolving AR eyewear, an emerging trillion-sensor economy, and powerful artificial intelligence.

As a result, we will soon be able to superimpose digital information atop any physical surrounding—freeing our eyes from the tyranny of the screen, immersing us in smart environments, and making our world endlessly dynamic.

In the third post of our five-part series on augmented reality, we will explore the convergence of AR, AI, sensors, and blockchain and dive into the implications through a key use case in manufacturing.

A Tale of Convergence
Let’s deconstruct everything beneath the sleek AR display.

It all begins with graphics processing units (GPUs)—electric circuits that perform rapid calculations to render images. (GPUs can be found in mobile phones, game consoles, and computers.)

However, because AR requires such extensive computing power, single GPUs will not suffice. Instead, blockchain can now enable distributed GPU processing power, and blockchains specifically dedicated to AR holographic processing are on the rise.

Next up, cameras and sensors will aggregate real-time data from any environment to seamlessly integrate physical and virtual worlds. Meanwhile, body-tracking sensors are critical for aligning a user’s self-rendering in AR with a virtually enhanced environment. Depth sensors then provide data for 3D spatial maps, while cameras absorb more surface-level, detailed visual input. In some cases, sensors might even collect biometric data, such as heart rate and brain activity, to incorporate health-related feedback in our everyday AR interfaces and personal recommendation engines.

The next step in the pipeline involves none other than AI. Processing enormous volumes of data instantaneously, embedded AI algorithms will power customized AR experiences in everything from artistic virtual overlays to personalized dietary annotations.

In retail, AIs will use your purchasing history, current closet inventory, and possibly even mood indicators to display digitally rendered items most suitable for your wardrobe, tailored to your measurements.

In healthcare, smart AR glasses will provide physicians with immediately accessible and maximally relevant information (parsed from the entirety of a patient’s medical records and current research) to aid in accurate diagnoses and treatments, freeing doctors to engage in the more human-centric tasks of establishing trust, educating patients and demonstrating empathy.

Image Credit: PHD Ventures.
Convergence in Manufacturing
One of the nearest-term use cases of AR is manufacturing, as large producers begin dedicating capital to enterprise AR headsets. And over the next ten years, AR will converge with AI, sensors, and blockchain to multiply manufacturer productivity and employee experience.

(1) Convergence with AI
In initial application, digital guides superimposed on production tables will vastly improve employee accuracy and speed, while minimizing error rates.

Already, the International Air Transport Association (IATA) — whose airlines supply 82 percent of air travel — recently implemented industrial tech company Atheer’s AR headsets in cargo management. And with barely any delay, IATA reported a whopping 30 percent improvement in cargo handling speed and no less than a 90 percent reduction in errors.

With similar success rates, Boeing brought Skylight’s smart AR glasses to the runway, now used in the manufacturing of hundreds of airplanes. Sure enough—the aerospace giant has now seen a 25 percent drop in production time and near-zero error rates.

Beyond cargo management and air travel, however, smart AR headsets will also enable on-the-job training without reducing the productivity of other workers or sacrificing hardware. Jaguar Land Rover, for instance, implemented Bosch’s Re’flekt One AR solution to gear technicians with “x-ray” vision: allowing them to visualize the insides of Range Rover Sport vehicles without removing any dashboards.

And as enterprise capabilities continue to soar, AIs will soon become the go-to experts, offering support to manufacturers in need of assembly assistance. Instant guidance and real-time feedback will dramatically reduce production downtime, boost overall output, and even help customers struggling with DIY assembly at home.

Perhaps one of the most profitable business opportunities, AR guidance through centralized AI systems will also serve to mitigate supply chain inefficiencies at extraordinary scale. Coordinating moving parts, eliminating the need for manned scanners at each checkpoint, and directing traffic within warehouses, joint AI-AR systems will vastly improve workflow while overseeing quality assurance.

After its initial implementation of AR “vision picking” in 2015, leading courier company DHL recently announced it would continue to use Google’s newest smart lens in warehouses across the world. Motivated by the initial group’s reported 15 percent jump in productivity, DHL’s decision is part of the logistics giant’s $300 million investment in new technologies.

And as direct-to-consumer e-commerce fundamentally transforms the retail sector, supply chain optimization will only grow increasingly vital. AR could very well prove the definitive step for gaining a competitive edge in delivery speeds.

As explained by Vital Enterprises CEO Ash Eldritch, “All these technologies that are coming together around artificial intelligence are going to augment the capabilities of the worker and that’s very powerful. I call it Augmented Intelligence. The idea is that you can take someone of a certain skill level and by augmenting them with artificial intelligence via augmented reality and the Internet of Things, you can elevate the skill level of that worker.”

Already, large producers like Goodyear, thyssenkrupp, and Johnson Controls are using the Microsoft HoloLens 2—priced at $3,500 per headset—for manufacturing and design purposes.

Perhaps the most heartening outcome of the AI-AR convergence is that, rather than replacing humans in manufacturing, AR is an ideal interface for human collaboration with AI. And as AI merges with human capital, prepare to see exponential improvements in productivity, professional training, and product quality.

(2) Convergence with Sensors
On the hardware front, these AI-AR systems will require a mass proliferation of sensors to detect the external environment and apply computer vision in AI decision-making.

To measure depth, for instance, some scanning depth sensors project a structured pattern of infrared light dots onto a scene, detecting and analyzing reflected light to generate 3D maps of the environment. Stereoscopic imaging, using two lenses, has also been commonly used for depth measurements. But leading technology like Microsoft’s HoloLens 2 and Intel’s RealSense 400-series camera implement a new method called “phased time-of-flight” (ToF).

In ToF sensing, the HoloLens 2 uses numerous lasers, each with 100 milliwatts (mW) of power, in quick bursts. The distance between nearby objects and the headset wearer is then measured by the amount of light in the return beam that has shifted from the original signal. Finally, the phase difference reveals the location of each object within the field of view, which enables accurate hand-tracking and surface reconstruction.

With a far lower computing power requirement, the phased ToF sensor is also more durable than stereoscopic sensing, which relies on the precise alignment of two prisms. The phased ToF sensor’s silicon base also makes it easily mass-produced, rendering the HoloLens 2 a far better candidate for widespread consumer adoption.

To apply inertial measurement—typically used in airplanes and spacecraft—the HoloLens 2 additionally uses a built-in accelerometer, gyroscope, and magnetometer. Further equipped with four “environment understanding cameras” that track head movements, the headset also uses a 2.4MP HD photographic video camera and ambient light sensor that work in concert to enable advanced computer vision.

For natural viewing experiences, sensor-supplied gaze tracking increasingly creates depth in digital displays. Nvidia’s work on Foveated AR Display, for instance, brings the primary foveal area into focus, while peripheral regions fall into a softer background— mimicking natural visual perception and concentrating computing power on the area that needs it most.

Gaze tracking sensors are also slated to grant users control over their (now immersive) screens without any hand gestures. Conducting simple visual cues, even staring at an object for more than three seconds, will activate commands instantaneously.

And our manufacturing example above is not the only one. Stacked convergence of blockchain, sensors, AI and AR will disrupt almost every major industry.

Take healthcare, for example, wherein biometric sensors will soon customize users’ AR experiences. Already, MIT Media Lab’s Deep Reality group has created an underwater VR relaxation experience that responds to real-time brain activity detected by a modified version of the Muse EEG. The experience even adapts to users’ biometric data, from heart rate to electro dermal activity (inputted from an Empatica E4 wristband).

Now rapidly dematerializing, sensors will converge with AR to improve physical-digital surface integration, intuitive hand and eye controls, and an increasingly personalized augmented world. Keep an eye on companies like MicroVision, now making tremendous leaps in sensor technology.

While I’ll be doing a deep dive into sensor applications across each industry in our next blog, it’s critical to first discuss how we might power sensor- and AI-driven augmented worlds.

(3) Convergence with Blockchain
Because AR requires much more compute power than typical 2D experiences, centralized GPUs and cloud computing systems are hard at work to provide the necessary infrastructure. Nonetheless, the workload is taxing and blockchain may prove the best solution.

A major player in this pursuit, Otoy aims to create the largest distributed GPU network in the world, called the Render Network RNDR. Built specifically on the Ethereum blockchain for holographic media, and undergoing Beta testing, this network is set to revolutionize AR deployment accessibility.

Alphabet Chairman Eric Schmidt (an investor in Otoy’s network), has even said, “I predicted that 90% of computing would eventually reside in the web based cloud… Otoy has created a remarkable technology which moves that last 10%—high-end graphics processing—entirely to the cloud. This is a disruptive and important achievement. In my view, it marks the tipping point where the web replaces the PC as the dominant computing platform of the future.”

Leveraging the crowd, RNDR allows anyone with a GPU to contribute their power to the network for a commission of up to $300 a month in RNDR tokens. These can then be redeemed in cash or used to create users’ own AR content.

In a double win, Otoy’s blockchain network and similar iterations not only allow designers to profit when not using their GPUs, but also democratize the experience for newer artists in the field.

And beyond these networks’ power suppliers, distributing GPU processing power will allow more manufacturing companies to access AR design tools and customize learning experiences. By further dispersing content creation across a broad network of individuals, blockchain also has the valuable potential to boost AR hardware investment across a number of industry beneficiaries.

On the consumer side, startups like Scanetchain are also entering the blockchain-AR space for a different reason. Allowing users to scan items with their smartphone, Scanetchain’s app provides access to a trove of information, from manufacturer and price, to origin and shipping details.

Based on NEM (a peer-to-peer cryptocurrency that implements a blockchain consensus algorithm), the app aims to make information far more accessible and, in the process, create a social network of purchasing behavior. Users earn tokens by watching ads, and all transactions are hashed into blocks and securely recorded.

The writing is on the wall—our future of brick-and-mortar retail will largely lean on blockchain to create the necessary digital links.

Final Thoughts
Integrating AI into AR creates an “auto-magical” manufacturing pipeline that will fundamentally transform the industry, cutting down on marginal costs, reducing inefficiencies and waste, and maximizing employee productivity.

Bolstering the AI-AR convergence, sensor technology is already blurring the boundaries between our augmented and physical worlds, soon to be near-undetectable. While intuitive hand and eye motions dictate commands in a hands-free interface, biometric data is poised to customize each AR experience to be far more in touch with our mental and physical health.

And underpinning it all, distributed computing power with blockchain networks like RNDR will democratize AR, boosting global consumer adoption at plummeting price points.

As AR soars in importance—whether in retail, manufacturing, entertainment, or beyond—the stacked convergence discussed above merits significant investment over the next decade. The augmented world is only just getting started.

Join Me
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This article originally appeared on Diamandis.com

Image Credit: Funky Focus / Pixabay Continue reading →

Posted in Human Robots

#435714 Universal Robots Introduces Its ...

Posted on September 17, 2019 by Android

Universal Robots, already the dominant force in collaborative robots, is flexing its muscles in an effort to further expand its reach in the cobots market. The Danish company is introducing today the UR16e, its strongest robotic arm yet, with a payload capability of 16 kilograms (35.3 lbs), reach of 900 millimeters, and repeatability of +/- 0.05 mm.

Universal says the new “heavy duty payload cobot” will allow customers to automate a broader range of processes, including packaging and palletizing, nut and screw driving, and high-payload and CNC machine tending.

In early 2015, Universal introduced the UR3, its smallest robot, which joined the UR5 and the flagship UR10, offering a payload capability of 3, 5, and 10 kg, respectively. Now the company is going in the other direction, announcing a bigger, stronger arm.

“With Universal joining its competitors in extending the reach and payload capacity of its cobots, a new standard of capability is forming,” Rian Whitton, a senior analyst at ABI Research, in London, tweeted.

Like its predecessors, the UR16e is part of Universal’s e-Series platform, which features 6 degrees of freedom and force/torque sensing on the tool flange. The UR family of cobots have stood out from the competition by being versatile in a variety of applications and, most important, easy to deploy and program. Universal didn’t release UR16e’s price, saying only that it is about 10 percent higher than that of the UR10e, which is about $50,000, depending on the configuration.

Jürgen von Hollen, president of Universal Robots, says the company decided to launch the UR16e after studying the market and talking to customers about their needs. “What came out of that process is we understood payload was a true barrier for a lot of customers,” he tells IEEE Spectrum. The 16 kg payload will be particularly useful for applications that require mounting specialized tools on the arm to perform tasks like screw driving and machine tending, he explains. Customers that could benefit from such applications include manufacturing, material handling, and automotive companies.

“We’ve added the payload, and that will open up that market for us,” von Hollen says.

The difference between Universal and Rethink

Universal has grown by leaps and bounds since its founding in 2008. By 2015, it had sold more than 5,000 robots; that number was close to 40,000 as of last year. During the same period, revenue more than doubled from about $100 million to $234 million. At a time when a string of robot makers have shuttered, including most notably Rethink Robotics, a cobots pioneer and Universal’s biggest rival, Universal finds itself in an enviable position, having amassed a commanding market share, estimated at between 50 to 60 percent.

About Rethink, von Hollen says the Boston-based company was a “good competitor,” helping disseminate the advantages and possibilities of cobots. “When Rethink basically ended it was more of a negative than a positive, from my perspective,” he says. In his view, a major difference between the two companies is that Rethink focused on delivering full-fledged applications to customers, whereas Universal focused on delivering a product to the market and letting the system integrators and sales partners deploy the robots to the customer base.

“We’ve always been very focused on delivering the product, whereas I think Rethink was much more focused on applications, very early on, and they added a level of complexity to their company that made it become very de-focused,” he says.

The collaborative robots market: massive growth

And yet, despite its success, Universal is still tiny when you compare it to the giants of industrial automation, which include companies like ABB, Fanuc, Yaskawa, and Kuka, with revenue in the billions of dollars. Although some of these companies have added cobots to their product portfolios—ABB’s YuMi, for example—that market represents a drop in the bucket when you consider global robot sales: The size of the cobots market was estimated at $700 million in 2018, whereas the global market for industrial robot systems (including software, peripherals, and system engineering) is close to $50 billion.

Von Hollen notes that cobots are expected to go through an impressive growth curve—nearly 50 percent year after year until 2025, when sales will reach between $9 to $12 billion. If Universal can maintain its dominance and capture a big slice of that market, it’ll add up to a nice sum. To get there, Universal is not alone: It is backed by U.S. electronics testing equipment maker Teradyne, which acquired Universal in 2015 for $285 million.

“The amount of resources we invest year over year matches the growth we had on sales,” von Hollen says. Universal currently has more than 650 employees, most based at its headquarters in Odense, Denmark, and the rest scattered in 27 offices in 18 countries. “No other company [in the cobots segment] is so focused on one product.”

[ Universal Robots ] Continue reading →

Posted in Human Robots

#435619 Video Friday: Watch This Robot Dog ...

Posted on September 13, 2019 by Android

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here’s what we have so far (send us your events!):

IEEE Africon 2019 – September 25-27, 2019 – Accra, Ghana
RoboBusiness 2019 – October 1-3, 2019 – Santa Clara, CA, USA
ISRR 2019 – October 6-10, 2019 – Hanoi, Vietnam
Ro-Man 2019 – October 14-18, 2019 – New Delhi, India
Humanoids 2019 – October 15-17, 2019 – Toronto, Canada
ARSO 2019 – October 31-1, 2019 – Beijing, China
ROSCon 2019 – October 31-1, 2019 – Macau
IROS 2019 – November 4-8, 2019 – Macau
Let us know if you have suggestions for next week, and enjoy today’s videos.

Team PLUTO (University of Pennsylvania, Ghost Robotics, and Exyn Technologies) put together this video giving us a robot’s-eye-view (or whatever they happen to be using for eyes) of the DARPA Subterranean Challenge tunnel circuits.

[ PLUTO ]

Zhifeng Huang has been improving his jet-stepping humanoid robot, which features new hardware and the ability to take larger and more complex steps.

This video reported the last progress of an ongoing project utilizing ducted-fan propulsion system to improve humanoid robot’s ability in stepping over large ditches. The landing point of the robot’s swing foot can be not only forward but also side direction. With keeping quasi-static balance, the robot was able to step over a ditch with 450mm in width (up to 97% of the robot’s leg’s length) in 3D stepping.

[ Paper ]

Thanks Zhifeng!

These underacuated hands from Matei Ciocarlie’s lab at Columbia are magically able to reconfigure themselves to grasp different object types with just one or two motors.

[ Paper ] via [ ROAM Lab ]

This is one reason we should pursue not “autonomous cars” but “fully autonomous cars” that never require humans to take over. We can’t be trusted.

During our early days as the Google self-driving car project, we invited some employees to test our vehicles on their commutes and weekend trips. What we were testing at the time was similar to the highway driver assist features that are now available on cars today, where the car takes over the boring parts of the driving, but if something outside its ability occurs, the driver has to take over immediately.

What we saw was that our testers put too much trust in that technology. They were doing things like texting, applying makeup, and even falling asleep that made it clear they would not be ready to take over driving if the vehicle asked them to. This is why we believe that nothing short of full autonomy will do.

[ Waymo ]

Buddy is a DIY and fetchingly minimalist social robot (of sorts) that will be coming to Kickstarter this month.

We have created a new arduino kit. His name is Buddy. He is a DIY social robot to serve as a replacement for Jibo, Cozmo, or any of the other bots that are no longer available. Fully 3D printed and supported he adds much more to our series of Arduino STEM robotics kits.

Buddy is able to look around and map his surroundings and react to changes within them. He can be surprised and he will always have a unique reaction to changes. The kit can be built very easily in less than an hour. It is even robust enough to take the abuse that kids can give it in a classroom.

[ Littlebots ]

The android Mindar, based on the Buddhist deity of mercy, preaches sermons at Kodaiji temple in Kyoto, and its human colleagues predict that with artificial intelligence it could one day acquire unlimited wisdom. Developed at a cost of almost $1 million (¥106 million) in a joint project between the Zen temple and robotics professor Hiroshi Ishiguro, the robot teaches about compassion and the dangers of desire, anger and ego.

[ Japan Times ]

I’m not sure whether it’s the sound or what, but this thing scares me for some reason.

[ BIRL ]

This gripper uses magnets as a sort of adjustable spring for dynamic stiffness control, which seems pretty clever.

[ Buffalo ]

What a package of medicine sees while being flown by drone from a hospital to a remote clinic in the Dominican Republic. The drone flew 11 km horizontally and 800 meters vertically, and I can’t even imagine what it would take to make that drive.

[ WeRobotics ]

My first ride in a fully autonomous car was at Stanford in 2009. I vividly remember getting in the back seat of a descendant of Junior, and watching the steering wheel turn by itself as the car executed a perfect parking maneuver. Ten years later, it’s still fun to watch other people have that experience.

[ Waymo ]

Flirtey, the pioneer of the commercial drone delivery industry, has unveiled the much-anticipated first video of its next-generation delivery drone, the Flirtey Eagle. The aircraft designer and manufacturer also unveiled the Flirtey Portal, a sophisticated take off and landing platform that enables scalable store-to-door operations; and an autonomous software platform that enables drones to deliver safely to homes.

[ Flirtey ]

EPFL scientists are developing new approaches for improved control of robotic hands – in particular for amputees – that combines individual finger control and automation for improved grasping and manipulation. This interdisciplinary proof-of-concept between neuroengineering and robotics was successfully tested on three amputees and seven healthy subjects.

[ EPFL ]

This video is a few years old, but we’ll take any excuse to watch the majestic sage-grouse be majestic in all their majesticness.

[ UC Davis ]

I like the idea of a game of soccer (or, football to you weirdos in the rest of the world) where the ball has a mind of its own.

[ Sphero ]

Looks like the whole delivery glider idea is really taking off! Or, you know, not taking off.

Weird that they didn’t show the landing, because it sure looked like it was going to plow into the side of the hill at full speed.

[ Yates ] via [ sUAS News ]

This video is from a 2018 paper, but it’s not like we ever get tired of seeing quadrupeds do stuff, right?

[ MIT ]

Founder and Head of Product, Ian Bernstein, and Head of Engineering, Morgan Bell, have been involved in the Misty project for years and they have learned a thing or two about building robots. Hear how and why Misty evolved into a robot development platform, learn what some of the earliest prototypes did (and why they didn’t work for what we envision), and take a deep dive into the technology decisions that form the Misty II platform.

[ Misty Robotics ]

Lex Fridman interviews Vijay Kumar on the Artifiical Intelligence Podcast.

[ AI Podcast ]

This week’s CMU RI Seminar is from Ross Knepper at Cornell, on Formalizing Teamwork in Human-Robot Interaction.

Robots out in the world today work for people but not with people. Before robots can work closely with ordinary people as part of a human-robot team in a home or office setting, robots need the ability to acquire a new mix of functional and social skills. Working with people requires a shared understanding of the task, capabilities, intentions, and background knowledge. For robots to act jointly as part of a team with people, they must engage in collaborative planning, which involves forming a consensus through an exchange of information about goals, capabilities, and partial plans. Often, much of this information is conveyed through implicit communication. In this talk, I formalize components of teamwork involving collaboration, communication, and representation. I illustrate how these concepts interact in the application of social navigation, which I argue is a first-class example of teamwork. In this setting, participants must avoid collision by legibly conveying intended passing sides via nonverbal cues like path shape. A topological representation using the braid groups enables the robot to reason about a small enumerable set of passing outcomes. I show how implicit communication of topological group plans achieves rapid covergence to a group consensus, and how a robot in the group can deliberately influence the ultimate outcome to maximize joint performance, yielding pedestrian comfort with the robot.

[ CMU RI ]

In this week’s episode of Robots in Depth, Per speaks with Julien Bourgeois about Claytronics, a project from Carnegie Mellon and Intel to develop “programmable matter.”

Julien started out as a computer scientist. He was always interested in robotics privately but then had the opportunity to get into micro robots when his lab was merged into the FEMTO-ST Institute. He later worked with Seth Copen Goldstein at Carnegie Mellon on the Claytronics project.

Julien shows an enlarged mock-up of the small robots that make up programmable matter, catoms, and speaks about how they are designed. Currently he is working on a unit that is one centimeter in diameter and he shows us the very small CPU that goes into that model.

[ Robots in Depth ] Continue reading →

Posted in Human Robots

#435601 New Double 3 Robot Makes Telepresence ...

Posted on September 12, 2019 by Android

Today, Double Robotics is announcing Double 3, the latest major upgrade to its line of consumer(ish) telepresence robots. We had a (mostly) fantastic time testing out Double 2 back in 2016. One of the things that we found out back then was that it takes a lot of practice to remotely drive the robot around. Double 3 solves this problem by leveraging the substantial advances in 3D sensing and computing that have taken place over the past few years, giving their new robot a level of intelligence that promises to make telepresence more accessible for everyone.

Double 2’s iPad has been replaced by “a fully integrated solution”—which is a fancy way of saying a dedicated 9.7-inch touchscreen and a whole bunch of other stuff. That other stuff includes an NVIDIA Jetson TX2 AI computing module, a beamforming six-microphone array, an 8-watt speaker, a pair of 13-megapixel cameras (wide angle and zoom) on a tilting mount, five ultrasonic rangefinders, and most excitingly, a pair of Intel RealSense D430 depth sensors.

It’s those new depth sensors that really make Double 3 special. The D430 modules each uses a pair of stereo cameras with a pattern projector to generate 1280 x 720 depth data with a range of between 0.2 and 10 meters away. The Double 3 robot uses all of this high quality depth data to locate obstacles, but at this point, it still doesn’t drive completely autonomously. Instead, it presents the remote operator with a slick, augmented reality view of drivable areas in the form of a grid of dots. You just click where you want the robot to go, and it will skillfully take itself there while avoiding obstacles (including dynamic obstacles) and related mishaps along the way.

This effectively offloads the most stressful part of telepresence—not running into stuff—from the remote user to the robot itself, which is the way it should be. That makes it that much easier to encourage people to utilize telepresence for the first time. The way the system is implemented through augmented reality is particularly impressive, I think. It looks like it’s intuitive enough for an inexperienced user without being restrictive, and is a clever way of mitigating even significant amounts of lag.

Otherwise, Double 3’s mobility system is exactly the same as the one featured on Double 2. In fact, that you can stick a Double 3 head on a Double 2 body and it instantly becomes a Double 3. Double Robotics is thoughtfully offering this to current Double 2 owners as a significantly more affordable upgrade option than buying a whole new robot.

For more details on all of Double 3's new features, we spoke with the co-founders of Double Robotics, Marc DeVidts and David Cann.

IEEE Spectrum: Why use this augmented reality system instead of just letting the user click on a regular camera image? Why make things more visually complicated, especially for new users?

Marc DeVidts and David Cann: One of the things that we realized about nine months ago when we got this whole thing working was that without the mixed reality for driving, it was really too magical of an experience for the customer. Even us—we had a hard time understanding whether the robot could really see obstacles and understand where the floor is and that kind of thing. So, we said “What would be the best way of communicating this information to the user?” And the right way to do it ended up drawing the graphics directly onto the scene. It’s really awesome—we have a full, real time 3D scene with the depth information drawn on top of it. We’re starting with some relatively simple graphics, and we’ll be adding more graphics in the future to help the user understand what the robot is seeing.

How robust is the vision system when it comes to obstacle detection and avoidance? Does it work with featureless surfaces, IR absorbent surfaces, in low light, in direct sunlight, etc?

We’ve looked at all of those cases, and one of the reasons that we’re going with the RealSense is the projector that helps us to see blank walls. We also found that having two sensors—one facing the floor and one facing forward—gives us a great coverage area. Having ultrasonic sensors in there as well helps us to detect anything that we can't see with the cameras. They're sort of a last safety measure, especially useful for detecting glass.

It seems like there’s a lot more that you could do with this sensing and mapping capability. What else are you working on?

We're starting with this semi-autonomous driving variant, and we're doing a private beta of full mapping. So, we’re going to do full SLAM of your environment that will be mapped by multiple robots at the same time while you're driving, and then you'll be able to zoom out to a map and click anywhere and it will drive there. That's where we're going with it, but we want to take baby steps to get there. It's the obvious next step, I think, and there are a lot more possibilities there.

Do you expect developers to be excited for this new mapping capability?

We're using a very powerful computer in the robot, a NVIDIA Jetson TX2 running Ubuntu. There's room to grow. It’s actually really exciting to be able to see, in real time, the 3D pose of the robot along with all of the depth data that gets transformed in real time into one view that gives you a full map. Having all of that data and just putting those pieces together and getting everything to work has been a huge feat in of itself.

We have an extensive API for developers to do custom implementations, either for telepresence or other kinds of robotics research. Our system isn't running ROS, but we're going to be adding ROS adapters for all of our hardware components.

Telepresence robots depend heavily on wireless connectivity, which is usually not something that telepresence robotics companies like Double have direct control over. Have you found that connectivity has been getting significantly better since you first introduced Double?

When we started in 2013, we had a lot of customers that didn’t have WiFi in their hallways, just in the conference rooms. We very rarely hear about customers having WiFi connectivity issues these days. The bigger issue we see is when people are calling into the robot from home, where they don't have proper traffic management on their home network. The robot doesn't need a ton of bandwidth, but it does need consistent, low latency bandwidth. And so, if someone else in the house is watching Netflix or something like that, it’s going to saturate your connection. But for the most part, it’s gotten a lot better over the last few years, and it’s no longer a big problem for us.

Do you think 5G will make a significant difference to telepresence robots?

We’ll see. We like the low latency possibilities and the better bandwidth, but it's all going to be a matter of what kind of reception you get. LTE can be great, if you have good reception; it’s all about where the tower is. I’m pretty sure that WiFi is going to be the primary thing for at least the next few years.

DeVidts also mentioned that an unfortunate side effect of the new depth sensors is that hanging a t-shirt on your Double to give it some personality will likely render it partially blind, so that's just something to keep in mind. To make up for this, you can switch around the colorful trim surrounding the screen, which is nowhere near as fun.

When the Double 3 is ready for shipping in late September, US $2,000 will get you the new head with all the sensors and stuff, which seamlessly integrates with your Double 2 base. Buying Double 3 straight up (with the included charging dock) will run you $4,ooo. This is by no means an inexpensive robot, and my impression is that it’s not really designed for individual consumers. But for commercial, corporate, healthcare, or education applications, $4k for a robot as capable as the Double 3 is really quite a good deal—especially considering the kinds of use cases for which it’s ideal.

[ Double Robotics ] Continue reading →

Posted in Human Robots

#435593 AI at the Speed of Light

Posted on September 11, 2019 by Android

Neural networks shine for solving tough problems such as facial and voice recognition, but conventional electronic versions are limited in speed and hungry for power. In theory, optics could beat digital electronic computers in the matrix calculations used in neural networks. However, optics had been limited by their inability to do some complex calculations that had required electronics. Now new experiments show that all-optical neural networks can tackle those problems.

The key attraction of neural networks is their massive interconnections among processors, comparable to the complex interconnections among neurons in the brain. This lets them perform many operations simultaneously, like the human brain does when looking at faces or listening to speech, making them more efficient for facial and voice recognition than traditional electronic computers that execute one instruction at a time.

Today's electronic neural networks have reached eight million neurons, but their future use in artificial intelligence may be limited by their high power usage and limited parallelism in connections. Optical connections through lenses are inherently parallel. The lens in your eye simultaneously focuses light from across your field of view onto the retina in the back of your eye, where an array of light-detecting nerve cells detects the light. Each cell then relays the signal it receives to neurons in the brain that process the visual signals to show us an image.

Glass lenses process optical signals by focusing light, which performs a complex mathematical operation called a Fourier transform that preserves the information in the original scene but rearranges is completely. One use of Fourier transforms is converting time variations in signal intensity into a plot of the frequencies present in the signal. The military used this trick in the 1950s to convert raw radar return signals recorded by an aircraft in flight into a three-dimensional image of the landscape viewed by the plane. Today that conversion is done electronically, but the vacuum-tube computers of the 1950s were not up to the task.

Development of neural networks for artificial intelligence started with electronics, but their AI applications have been limited by their slow processing and need for extensive computing resources. Some researchers have developed hybrid neural networks, in which optics perform simple linear operations, but electronics perform more complex nonlinear calculations. Now two groups have demonstrated simple all-optical neural networks that do all processing with light.

In May, Wolfram Pernice of the Institute of Physics at the University of Münster in Germany and colleagues reported testing an all-optical “neuron” in which signals change target materials between liquid and solid states, an effect that has been used for optical data storage. They demonstrated nonlinear processing, and produced output pulses like those from organic neurons. They then produced an integrated photonic circuit that incorporated four optical neurons operating at different wavelengths, each of which connected to 15 optical synapses. The photonic circuit contained more than 140 components and could recognize simple optical patterns. The group wrote that their device is scalable, and that the technology promises “access to the high speed and high bandwidth inherent to optical systems, thus enabling the direct processing of optical telecommunication and visual data.”

Now a group at the Hong Kong University of Science and Technology reports in Optica that they have made an all-optical neural network based on a different process, electromagnetically induced transparency, in which incident light affects how atoms shift between quantum-mechanical energy levels. The process is nonlinear and can be triggered by very weak light signals, says Shengwang Du, a physics professor and coauthor of the paper.

In their demonstration, they illuminated rubidium-85 atoms cooled by lasers to about 10 microKelvin (10 microdegrees above absolute zero). Although the technique may seem unusually complex, Du said the system was the most accessible one in the lab that could produce the desired effects. “As a pure quantum atomic system [it] is ideal for this proof-of-principle experiment,” he says.

Next, they plan to scale up the demonstration using a hot atomic vapor center, which is less expensive, does not require time-consuming preparation of cold atoms, and can be integrated with photonic chips. Du says the major challenges are reducing cost of the nonlinear processing medium and increasing the scale of the all-optical neural network for more complex tasks.

“Their demonstration seems valid,” says Volker Sorger, an electrical engineer at George Washington University in Washington who was not involved in either demonstration. He says the all-optical approach is attractive because it offers very high parallelism, but the update rate is limited to about 100 hertz because of the liquid crystals used in their test, and he is not completely convinced their approach can be scaled error-free. Continue reading →

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